The present invention relates to self-repairing networks, and it relates, more particularly, to modified networks for producing a neural network exhibiting the ability to repair itself upon faults of its constituent processing elements while correctly interpreting input data subject to noise or errors.
Parallel processors or processor arrays or matrices are known and beginning to be used in a variety of fields, ranging from numerical methods and robotics, to cognitive processes such as vision and speech sensing and interpretation. A set or collection of a number of identical processing elements connected in a network capable of storing information and processing information can provide a significant signal processing function known as contrast enhancement. Such collections can be combined to form a trellis network wherein processing elements are located at the nodes functionally equivalent to an underlying trellis code graphically represented by interconnection of processing elements at its coordinates. Mathematically, the operation of such a collection can be described by bilinear differential equations. Alternatively, this operation may be viewed as solving for a stable point of a system of bilinear differential equations. Recent advances in very large scale integration (VLSI) technology have made it possible to construct concurrent processor arrays based on a regular array of locally connected processing elements or cells. Besides their obvious useful applications, such processor arrays have the potential of exhibiting behavior characterized by self-organization, learning and recognition.
Even a single layer network including a large number of array cells and associated control presents a structure that is rather complex in terms of architecture and operation. By interconnecting the cells in a trellis network designed in accordance with a convolutional code storing a received sequence, the trellis network is able to minimize a function that is analogous to the log likelihood function for the received sequence near the global minimum Simulations demonstrate that such a network in a paper entitled "A Trellis-Structured Neural Network" by T. Petsche and B. W. Dickinson, NIPS, pp. 592-601, American Institute of Physics, NY, 1988 can successfully decode input sequences containing no noise as well as a globally connected decomposition network or an optimum decoder both of which present considerable fabrication difficulties. In addition, for low error rates, this network can also decode noisy received sequences.